Wind power is considered one of the cleanest, most environmentally friendly energy sources presently available, and wind turbines have gained increased attention in this regard. A modern wind turbine typically includes a tower, a generator, a gearbox, a nacelle, and one or more rotor blades. The rotor blades capture kinetic energy of wind using known foil principles. The rotor blades transmit the kinetic energy in the form of rotational energy so as to turn a low-speed main shaft coupling the rotor blades to a gearbox, or if a gearbox is not used, directly to the generator. For example, the generator may be coupled to the low-speed main shaft such that rotation of the shaft drives the generator. For instance, the generator may include a high-speed generator shaft rotatably coupled to the main shaft through the gearbox. The generator then converts the mechanical energy from the rotor to electrical energy that may be deployed to a utility grid.
In addition, modern wind turbines include a plurality of high-speed and low-speed bearings to provide rotation of the various components thereof. For example, the low-speed main shaft typically includes one or more main bearings mounted at a forward and rearward end thereof to allow the low-speed main shaft to rotate about an axis. Further, the gearbox may include multiple bearings for providing the desired rotation of the various gears therein. More specifically, the gearbox generally includes low-speed carrier bearings and low-speed planet bearings.
Detection of damaged bearings in a wind turbine is essential in minimizing unplanned downtime of the turbine and increasing turbine availability. One conventional damage detection approach relies on the enveloping spectrum of the main bearing and the planetary stage gearbox sensors. Though the primary fault frequencies for the inner race ball pass (IRBP) and the outer race ball pass (ORBP) in the enveloping spectrum have historically been used as a strong indicator for bearing damage of intermediate-speed and high-speed bearings, such frequencies often do not provide enough clarity for low-speed bearing damage.
Visual detection of bearing fault frequency harmonics has proven successful in locating damaged components; however, this approach relies on the consistent manual inspection of the spectrums. Such inspection is inherently time consuming and can result in missed detection of failed components. In addition, although manual inspection methods have been utilized with success, such methods do not provide a scalable option and result in reduced monitoring efficiency.
For at least the aforementioned reasons, the detection of low-speed bearing damage has proven difficult to automate using traditional detection analytics and/or trending techniques. For low-speed planetary bearings in particular, there is currently no known method which can consistently and accurately detect and trend the energy of bearing damage propagation using traditional fast Fourier transform spectral analysis techniques.
Accordingly, improved systems and methods for detecting damage in low-speed wind turbine bearings would be desired in the art.